Isoform-specific functions of Numb in breast cancer progression, metastasis and proteome remodeling
Bibliographic record
Abstract
ABSTRACT Deregulated alternative splicing of the endocytic adaptor NUMB resulting in high expression of Exon9in (exon 9-containing) isoforms has been reported in several cancer types. However, the role of Numb isoform expression in tumor progression and the underlying mechanisms remain elusive. Here, we report greater exon 9 inclusion in multiple cancer types including all subtypes of breast cancer, and correlation of higher exon 9 inclusion in patients with worse prognosis. Deletion of Exon9in in breast cancer cells leads to reduced cell growth and a significant decrease of lung metastasis in orthotopic xenograft experiments. Quantitative mass spectrometry revealed downregulation of proteins involved in EMT and ECM organization and remodeling of the endocytic protein network in cells lacking the Exon9in Numb isoforms. Exon 9 deletion also results in reduced surface levels of ITGβ5, and downstream signaling to ERK and SRC, consistent with enhance lysosomal targeting mediated by the remaining Exon9sk (exon 9 skipping) Numb isoforms. SIGNIFICANCE Expression of NUMB Exon9in protein isoforms correlate with worse progression free survival, particularly in breast cancer. Our findings also reveal that Exon9in isoforms promote breast cancer progression by relieving Numb mediated down regulation of integrins and implicate Numb alternative splicing as a progression factor in multiple cancer types.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".